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<p>One of the main challenges with optical remote sensing imagery is the lack of sufficient data and the difficulty in collecting large amounts of images. Since the remote sensing field depends on large datasets for accurate processing by machine learning models, it is essential to have a&#xa0;greater number of data samples to enable effective AI-based processing in this domain. To address this issue, we propose generative models that can synthesize realistic texture images from limited data. This paper presents a&#xa0;novel approach to enhance texture image synthesis across high-, medium-, and low-resolution remotely sensed images, which encompasses both regular and irregular texture types. The main idea is to integrate texture descriptors into the attention mechanisms of generative models, such as the Texture-based ViTGAN model (Tex-ViTGAN) and the Texture-based Diffusion model (Tex-Diffusion), to improve the extraction of texture-specific features while preserving spatial arrangement, structural patterns, and fine details. The Tex-ViTGAN model has limitations in preserving structural patterns in regular textures, so the Tex-Diffusion model overcomes this problem and significantly improves texture representation for regular and irregular textures at all resolution levels of remotely sensed images. To validated the performance of our proposed methods, we used standard evaluation metrics to assess the quality of generated images by calculating the Fréchet Inception Distance (FID), Inception Score (IS), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) between generated and real image samples. Our Tex-Diffusion model achieved FID of&#xa0;5.1, IS of 7.94, SSIM of 0.90, and LPIPS of 0.177 on an irregular remotely sensed sample, demonstrating comparable results to the state-of-the-art models.</p>

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AttentionTex: Attention-Guided Texture Synthesis on Remotely Sensed Images

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Elahe Salari,
  • Zohreh Azimifar,
  • Maryam Dehghani

摘要

One of the main challenges with optical remote sensing imagery is the lack of sufficient data and the difficulty in collecting large amounts of images. Since the remote sensing field depends on large datasets for accurate processing by machine learning models, it is essential to have a greater number of data samples to enable effective AI-based processing in this domain. To address this issue, we propose generative models that can synthesize realistic texture images from limited data. This paper presents a novel approach to enhance texture image synthesis across high-, medium-, and low-resolution remotely sensed images, which encompasses both regular and irregular texture types. The main idea is to integrate texture descriptors into the attention mechanisms of generative models, such as the Texture-based ViTGAN model (Tex-ViTGAN) and the Texture-based Diffusion model (Tex-Diffusion), to improve the extraction of texture-specific features while preserving spatial arrangement, structural patterns, and fine details. The Tex-ViTGAN model has limitations in preserving structural patterns in regular textures, so the Tex-Diffusion model overcomes this problem and significantly improves texture representation for regular and irregular textures at all resolution levels of remotely sensed images. To validated the performance of our proposed methods, we used standard evaluation metrics to assess the quality of generated images by calculating the Fréchet Inception Distance (FID), Inception Score (IS), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) between generated and real image samples. Our Tex-Diffusion model achieved FID of 5.1, IS of 7.94, SSIM of 0.90, and LPIPS of 0.177 on an irregular remotely sensed sample, demonstrating comparable results to the state-of-the-art models.